Spatial Demography最新文献

A geospatial model was developed to statistically assess the socioeconomic effects of coal mining in the greater Pittsburgh, Pennsylvania area by integrating home sale data, abandoned mine lands (AML) inventory “problem area” sites, and census demographic information. Results indicated that homes located within problem areas sold for an average of 28% ($58,600) less than homes outside of these regions. Demographic data revealed a notable disparity in the population distribution within Allegheny County mining problem areas as having a statistically significant larger Black population. This same trend was even more pronounced in urban areas. The study also established that areas influenced by past mining activities had a higher proportion of individuals without formal postsecondary education. Logistic regression models were created to analytically evaluate the relationship between predictor variables, specifically home sale price and Community Needs Index, to the probability of being situated within mining problem areas. The home sale analysis indicated a negative correlation between sale prices and the likelihood of residing in a mining-affected zone, implying that properties with lower prices are more commonly situated in these impacted areas. The CNI logistic regression model revealed a correlation between the probability of living in a mining problem area and overall higher community needs.

One potential explanation for the continuance of spousal violence in India is spousal violence spatial spillover effects, in which the occurrence of spousal violence in one family or area increases the incidence of spousal violence in a neighboring one. This study used the 2019–2021 Indian Demographic and Health Survey data to examine the hypothesis of a spillover effect of spousal violence in India and to ascertain a thorough analysis of the geographic relationship between hypothetical origin place of spousal violence and its neighbors, and the occurrence of spousal violence. Our multivariate spatial auto regressive model further suggests that in India there has spatial spillover effect of neighborhood characteristics on spousal violence. The findings imply that concentrating legislative initiatives to diminish spousal violence in one area may decrease the incidence in nearby areas, resulting in an overall reduction across the country. The current study argues that a shift away from country-level policies to localized strategies targeting on specific geographic clusters may be a more cost-effective but may be considered an efficient means for reducing spousal violence against women in India.

Adverse sexual and reproductive health outcomes resulting from risky sexual behaviours (RSB) among young adults disproportionately affect them and contribute to an increased burden of sexually transmitted diseases. Existing research in sub-Saharan Africa, has yielded contrasting findings on the factors associated with the sexual behaviour of young people. This paper presents a comprehensive examination of the geographical distribution and socio-demographic determinants of RSB among young people in Nigeria, focusing on three primary outcomes: unprotected sex, multiple sexual partners, and early sexual debut. This study used cross-sectional data from the 2018 Nigeria Demographic and Health Survey on young adults aged 15–24 years. Descriptive and inferential statistics, including Bayesian multivariate shared component spatial modelling were used to assess geographical and socio-demographic factors influencing RSB. Males in the North-West region exhibited a high prevalence of unprotected sex and early sexual debut while unprotected sex was more prevalent among females in the South–South region. Furthermore, individuals in the South-South and Central regions consistently showed a higher likelihood of engaging in all three indicators of RSB. Individuals who were employed as well as rural residents had an increased risk of early sexual debut and multiple sexual partners. Furthermore, mobile phone and internet access were found to impact RSB. These findings offer insights into the geographic distribution and socio-demographic determinants of RSB among young people, highlighting the need for tailored interventions. These insights can inform the development of targeted interventions, education programs, and policies to reduce the prevalence of RSB.

The implementation of place-based policies entails the construction of intervention areas (spatially contiguous areas in which the policies are adopted). Many approaches can be adopted for the definition of such areas. This paper reflects on the use of geographically weighted regression (GWR) models as a tool capable of supporting the definition process. The case study concerns Molise, a region in Southern Italy particularly affected by persistent and deep-rooted processes of depopulation. The dependent variable is the average annual rate of population change of municipalities of Molise across the 2011–2019. The independent variables are related to socio-economic profiles of each municipality. The results, contextualised using a broad overview of the Italian case, show that a key variable in the demographic dynamics of the municipalities of Molise is the labour market activity rate of women and that this variable drives a spatial instability that cannot be detected using global approaches and models. This proves the urgent need to expand the use of local thinking for the benefit of both applied demography and society.

Modern computational capabilities have brought about concerns about risks associated with the level of information disclosed in public datasets. A tension exists between making data available that protects the confidentiality of individuals while containing sufficiently detailed geographic information to underpin the utility of research. Our aim is to inform data collectors and suppliers about geographic choices for confidentiality protection and to balance this with reassurance to the research community that data will still be fit-for-purpose. We test this using simple logistic regression models, by investigating the interplay between two geographical entities (points for the observations and polygons for area attributes) at a variety of scales, using a synthetic population of 22,000 people. In an England and Wales setting, we do this for individuals located by postcodes and by postal sector and postal district centroids and link these to a variety of census geographies. We also ‘jitter’ postcode coordinates to test the effect of moving people away from their original location. We find a smoothing of relationships up the geographical hierarchy. However, if postal sector centroids are used to locate individuals, linkages to Lower/Medium Super Output Area scales and subsequent results are very similar to the more detailed unit postcodes. Postcode locations jittered by 500–750 m in any direction are likely to allow the same conclusions to be drawn as for the original locations. Within these geographic scenarios, there is likely to be a sufficient level of confidentiality protection while statistical relationships are very similar to those obtained using the most detailed geographic locators.